Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Derivative-enhanced Deep Operator Network
Authors: Yuan Qiu, Nolan Bridges, Peng Chen
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments validate the effectiveness of our approach. |
| Researcher Affiliation | Academia | Yuan Qiu, Nolan Bridges, Peng Chen Georgia Institute of Technology, Atlanta, GA 30332 EMAIL |
| Pseudocode | No | The paper does not contain pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | The code for data generation, model training and inference, as well as configurations to reproduce the results in this paper can be found at https://github.com/qy849/DE-Deep ONet. |
| Open Datasets | No | We generate Ntrain = 1500 and Ntest = 500 input-output pairs (m(i), u(i)) for training and testing, respectively. |
| Dataset Splits | No | The paper specifies training and test sets but does not explicitly mention a separate validation set split. |
| Hardware Specification | Yes | Table 4: Wall clock time (seconds/iteration with batch size 8) for training on a single NVIDIA RTX A6000 GPU; Table 2: Wall clock time (in seconds) for data generation on 2 AMD EPYC 7543 32-Core Processors |
| Software Dependencies | No | The paper mentions software like FEniCS [31] and hIPPYlib [28], as well as torch.func.jacrev and torch.vmap (implying PyTorch), but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | We train each model for 32768 iterations (with the same batch size 8) using an Adam W optimizer [34] and a Step LR learning rate scheduler (We disable learning rate scheduler for DE-Deep ONet). |